ThesisPDF Available

Investigating Potentially Harmful Applications on Android


Abstract and Figures

This paper talks about the ever increasing number of malware-riddled applications on the Android Play Store, and Google's lack of effective measures to counter them. I will propose and execute a proof-of-concept of my solution, which involves testing a random sample of 60 applications per category, using a two-pronged approach in identifying potentially harmful applications by performing robust regression of their permissions to find outliers, and then confirming these outliers by comparing the amount of traffic sent of a control phone with few contacts and a test phone with hundreds of contacts. A value is then assigned as a gauge of their potential risk and published towards the end of this paper.
Content may be subject to copyright.
Investigating Potentially Harmful
Applications on Android
Chief Examiner:
Dr. Keiji TAKEDA
A thesis submitted in fulfillment of the requirements
for the degree of Bachelor of Arts, Environment and Information Studies
in the
takeda lab. ISC
Jun Murai’s Internet Research Lab
July 25, 2018
Faculty of Environment and Information Studies
takeda lab. ISC
Bachelor of Arts, Environment and Information Studies
Investigating Potentially Harmful Applications on Android
This paper talks about the ever increasing number of malware-riddled applications
on the Android Play Store, and Google’s lack of effective measures to counter them.
I will propose and execute a proof-of-concept of my solution, which involves testing
a random sample of 60 applications per category, using a two-pronged approach
in identifying potentially harmful applications by performing robust regression of
their permissions to find outliers, and then confirming these outliers by comparing
the amount of traffic sent of a control phone with few contacts and a test phone with
hundreds of contacts. A value is then assigned as a gauge of their potential risk and
published towards the end of this paper.
Keywords: Android, Playstore, Google Play, PHA, Potentially Harmful Apps, Mal-
ware, Privacy, Mobile communication, computer network security, invasive soft-
ware, mobile computing, public domain software, telecommunication traffic, trans-
port protocols, personal information, HTTP traffic, statistical information, mobile
bots, x86 malware
First and foremost, I would like to thank my project supervisor and advisor, Keiji
Takeda, for providing me an opportunity to research on mobile security, a field that
most undergraduate students do not get exposed to. Your patience and advice has
been invaluable in the successful completion of this project.
Secondly, I would like to thank my family, especially my Mum and Dad for let-
ting me become fully independent at an age younger than all of my peers. Thank
you for not giving up on me back when I dropped out of high school, and for en-
couraging me to pursue my interest in computers. Thank you for believing in me,
and for letting me come to Japan despite never having taken Japanese lessons. Your
support has led me to become what I am today. I would also like to thank my little
sister, Angela, for inspiring me to try living overseas.
Thirdly, I would like to thank my friends from Singapore, who have spurred
me on with their excellence in academia, especially Ian who made it to CERN; and
Matthias and DS, who have cheered me up during my darkest moments. Also, spe-
cial thanks to Raynold, for introducing me to the real life applications of cyber secu-
Fourthly, I would like to express appreciation for other members of ISC - Ko-
rry, my Eigo Jedi friend from Hawaii, and Jojo, Aaron and Bradley for all the pasta
sessions we have had.
Lastly, I would also like to thank my peers in the GIGA Program, particularly
Nick and Ival, who have been my comrades in arms, as well as Jie, Jake and Shiina.
I will never forget the times we have spent together and the experiences we have
shared. You guys have made living in Japan a very fun experience.
Abstract ii
Acknowledgements iii
1 Introduction 1
1.1 AbouttheAuthor............................... 1
1.2 Inspirations .................................. 1
1.3 Contributions ................................. 2
1.4 ThesisStructure................................ 2
2 Background 3
2.1 Information .................................. 3
2.1.1 MarketShare ............................. 3
2.1.2 Android Malware Trends . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Malware .................................... 4
2.2.1 TypesandFamilies.......................... 4
2.2.2 AttackVectors............................. 4
2.3 AnalysisMethods............................... 5
2.3.1 Long Tail Outlier Analysis . . . . . . . . . . . . . . . . . . . . . . 5
2.4 ExistingWorks................................. 6
2.4.1 Detecting Malware Leveraging Text Semantics of Network Flows 6
2.4.2 Data Mining of Permissions Mode . . . . . . . . . . . . . . . . . 6
2.4.3 Explaining Black-box Android Malware Detection . . . . . . . . 6
3 Key Ideas and Objective 7
3.1 Problem .................................... 7
3.2 Hypothesis................................... 7
3.3 Goal....................................... 7
4 Design and Implementation 8
4.1 Environment.................................. 8
4.2 Two-pronged Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.2.1 First prong - Permissions Frequency Analysis . . . . . . . . . . 11
4.2.2 Second prong - Traffic analysis . . . . . . . . . . . . . . . . . . . 15
4.3 Limitations................................... 17
5 Results and Evaluation 18
5.1 Results: Comics Category . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.1.1 Permission Frequency Analysis . . . . . . . . . . . . . . . . . . . 18
5.1.2 Content Length Analysis . . . . . . . . . . . . . . . . . . . . . . 19
5.1.3 Rating ................................. 20
5.2 Results: Weather Category . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2.1 Permission Frequency Analysis . . . . . . . . . . . . . . . . . . . 21
5.2.2 Content Length Analysis . . . . . . . . . . . . . . . . . . . . . . 22
5.2.3 Rating ................................. 23
5.3 Results: Dating Category . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.3.1 Permission Frequency Analysis . . . . . . . . . . . . . . . . . . . 24
5.3.2 Content Length Analysis . . . . . . . . . . . . . . . . . . . . . . 25
5.3.3 Rating ................................. 26
5.4 OverallEvaluation .............................. 27
6 Conclusion 28
6.1 Summary.................................... 28
6.2 Futurework .................................. 28
6.3 Closingthoughts ............................... 29
A Frequently Asked Questions 30
B Glossary 31
List of Figures
2.3.1 Long Tail Analysis Using Power Law [20]................. 5
4.1.1 Proxy Listener Setting on Computer . . . . . . . . . . . . . . . . . . . . 8
4.1.2 Setting up proxy on phone . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1.3 Tasker Profile on Phone . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2.1 Raw Permissions Data (Comics) . . . . . . . . . . . . . . . . . . . . . . . 13
5.1.1 Permissions Frequency Analysis (Comics) . . . . . . . . . . . . . . . . . 18
5.1.2 Content Length Analysis (Comics) . . . . . . . . . . . . . . . . . . . . . 19
5.1.3 Percentage Difference (Comics) . . . . . . . . . . . . . . . . . . . . . . . 20
5.2.1 Permissions Frequency Analysis (Weather) . . . . . . . . . . . . . . . . 21
5.2.2 Content Length Analysis (Weather) . . . . . . . . . . . . . . . . . . . . . 22
5.2.3 Percentage Difference (Weather) . . . . . . . . . . . . . . . . . . . . . . 23
5.3.1 Permissions Frequency Analysis (Dating) . . . . . . . . . . . . . . . . . 24
5.3.2 Content Length Analysis (Dating) . . . . . . . . . . . . . . . . . . . . . 25
5.3.3 Percentage Difference (Dating) . . . . . . . . . . . . . . . . . . . . . . . 26
List of Tables
4.1 Permissions Frequency (Comics) . . . . . . . . . . . . . . . . . . . . . . 14
List of Abbreviations
APK Application Package Kit
HTTP Hyper Text Transfer Protocol
HTTPS Hyper Text Transfer Protocol Secure
IAP InApp Purchase
IP Internet Protocol
NLP Natural Language Processing
OS Operating System
OSS Open Source Software
OOTB Out OfThe Box
PHA Potentially Harmful Applications
SDK Software Development Kit
SNS Social Networking Sites
SVM Support Vector Machine
SSL/TLS Secure Socket Layer / Transport Security
VPN Virtual Private Network
List of Symbols
percentage difference %
This is a disclaimer stating that this project is done for research
purposes only. My research was done in goodwill for the
advancement of science and I never had, and never have any
intention to defame any applications or companies. Assigned
ratings do not necessarily mean that the application is
definitely a malware; it only a possibility that it has been
exhibiting potentially harmful behavior.
Chapter 1
1.1 About the Author
The author of this paper is a fourth year undergraduate student of Keio University’s
Environment and Information Studies in Tokyo, Japan. He belongs to the Takeda
Laboratory under Jun Murai’s WIDE Group, with a focus on Cyber Security research
under Professor Takeda.
1.2 Inspirations
The work presented in this paper was inspired by previous works on malware de-
tection and analysis on the Android platform, and more generally, the works of soft-
ware developers with the aim of protecting consumer privacy, such as those of Ad-
block and uBlock Origin ([8]). The Android market has always interested me, seeing
as there to be relatively few people working on it as the common misconception
seems to be that only rooted phones are vulnerable to malware, or they could only
get infected through third party application markets. However, from the Expensive-
Wall ([4]) and Judy ([2]) malware outbreak that occurred just in 2017, we can see that
it was not true.
I would also like to touch on four misconceptions that also served as an inspira-
tion for my topic:
Firstly, antivirus apps does not protect against viruses. By the time it is on the
phone, the system has already been infected and it will be too late to rectify. More-
over, even the top and leading antivirus apps have a very high false negative rate
[22], and have low detection rates for actual malware, which leads us to question
their detection methods. In addition to that, the top antivirus apps all have critical
vulnerabilities, and none of the promoted security features are even sufficiently se-
cure. [17] It has been proven possible, and very easy to disable the malware-scanning
engine remotely or access confidential data via privilege escalation vulnerabilities
such as broken SSL communication and self-developed crypto implementations.
Secondly, malware is not restricted only to rooted phones. As history has shown
us[7], there have been outbreaks of malware in the Google Playstore which has af-
fected many non-rooted phones. Also, the Google Playstore is just as vulnerable to
malware as are third party application markets.
Thirdly, while not directly related to malware, firewall apps on the Android
platform essentially acts as a VPN with traffic filtering options, requiring end-user
knowledge in customizing these filters.
Thus, even with a top firewall app and a leading antivirus, neither would prevent
PHAs from infecting the phone or sending privacy-violating information about the
2Chapter 1. Introduction
1.3 Contributions
This research lays the basis for readers to do their own quantitative research on the
Android Playstore. Upon completion, researchers would be able to adopt the pro-
posed methodology used in order to analyze the entire application ecosystem, i.e.
including the unofficial third party ones such as Aptoide. By using a physical de-
vice with the implementations of automation, researchers will find that the project
is scalable, and can further expand upon this paper by using hosted cloud servers to
gather the traffic data from multiple applications at the same time, thereby improv-
ing on the accuracy of the analysis by changing the time into a constant non-variable,
thus streamlining the equation. Research scholars will be able to understand the dif-
ferences between data gathering with a physical device and an emulated one, in
that smart malware using user-agent checks to circumvent malware detection can
be observed in its natural state.
The source code for the implementation has been made open source on Github.1
1.4 Thesis Structure
This thesis is structured as follows:
Chapter 1 is a brief introduction of the author’s affiliations.
Chapter 2 gives the reader a cursory background of the Android platform and
its importance in the relation to the whole market, as well as consumer behavior.
It also mentions the existing works and how the author differentiates his thesis by
improving on them and exploring the analysis from a new angle.
Chapter 3 introduces the key ideas and the formulation thereof. The main ob-
jective of the project is defined, which is to publish the a comprehensive statistic of
potentially harmful applications (PHAs) in the Play Store.
Chapter 4 illustrates the implementation of the author’s ideas using a two-pronged
approach by firstly filtering applications that need to be analyzed meticulously, and
then describing the utilization of Content Length Analysis as a means to analyze
traffic flows.
Chapter 5 presents the results obtained from the experiments. Ratings are as-
signed linearly based on a histogram charted from the percentage difference in traf-
fic volume when comparing between the real and test cases. A total number of
percentage of applications per category is then published and evaluated for error.
Chapter 6 closes the paper by summing up the findings and yielding the conclu-
sion drawn from the evaluation. It also includes future work that the author hopes
to accomplish.
1Investigating Content Length:
Chapter 2
This chapter will introduce the basic concepts and conceived justifications that will
be utilized the investigation.
2.1 Information
2.1.1 Market Share
Android is currently the most used smart phone mobile device platform globally,
taking up 85.0% of the market share [11], followed by iOS at 14.7% - a large differ-
ence. As of now, there are over 3.7 million applications on the Play Store, with over
0.9% of them having over a million downloads. Monetization SDKs serving adver-
tisements and sending user data are dominating the top 10 embedded SDK list, and
the latest OS released almost a year ago still has not reached 5% penetration ([1]).
As of the end of last year, there are over 2.3 billion mobile phone users [21]. 85%
of these would make up over 25% of the world’s population, making it the second
most consumed consumer market, second only to computer ownership that lies at
38%[6]. With the ever-growing market, it is critical that we treat threats to the general
safety of its users as a cause for concern, which is the aim of this project.
2.1.2 Android Malware Trends
There are two billion monthly active devices running Android. In 2014, there were
more than 650,000 individual pieces of malware for Android discovered. In 2016,
PHAs (potentially harmful apps) were on 0.05% of devices compared to 0.15% in
2015. The raw percentage has decreased, but with the increase in devices, in reality
it is still affecting millions of users. Furthermore, in 2017 alone there were over 40
million devices infected by the ExpensiveWall and Judy malware[7].
These frequent malware outbreaks occur when a group of applications slip through
Play Protect’s scanning. Bypassing Play Protect’s scanning via various methods such
as adding delays between code execution as elementary and thus it cannot be re-
lied on to provide a safe environment for consumers. As such, this paper aims to
bring attention to the risks users are putting themselves in by trusting Google’s Play
Protect as an all-round form of protection, which has no safeguard against privacy-
violating applications. With that said, there is a general lack of Internet Law that
prevents such issues from occurring, which is another problem in itself [10].
4Chapter 2. Background
2.2 Malware
2.2.1 Types and Families
There are many types of malware and they can be grouped into different families;
however, this thesis will focus on those related to the network and traffic communi-
cations. Botnets is one of the common family types when analyzing network flows,
however this thesis will be putting its focus on the actual content of the traffic packet
as opposed to a specific family.
2.2.2 Attack Vectors
Gullible users is the first thing that comes to mind when talking about security. No
matter how good a system may be, users are the weakest link when it depends on
them to make decisions [9], such as whether to grant permissions to use an app,
or whether to give personal information such as identification cards or credit card
information for the purpose of verification, without first reading the lengthy privacy
policy and terms of use.
Gaming the system with ranking fraud is also another commonly used tactic. In
the Android Play Store and other markets, certain statistics such as user reviews and
ratings, number of downloads, search frequency, page views are used to determine
the ranking of an app [23]. Out of those, at least three - user reviews, ratings and
downloads can be controlled by the app developer by either developing bots or by
paying third-party sites to boost these statistics. This is exactly what happened with
the ExpensiveWall and Judy malware[7] - when a certain download/rating thresh-
old is reached, users tend to trust those apps even if the apps are newly published
Misplaced trust in OSS is also another vector. While it is true that code may
be reviewed when it is open-source, it is often not as there are far more developers
writing code than there are those validating them - it is a fact that the code is not
reviewed when uploaded to the Play Store; moreover, the burden of trust is placed
upon the user, which is a justification of this point. In the most recent reported case
[5], the ARC Welder Chrome extension app, which has over 900,000 downloads, has
become a victim where the fake extension’s developer has managed to remove the
original app from the search engine indexer - i.e. when a user attempts to search for
the app, only the fake one appears. In just a few days, it has achieved 32,000 installs
and Google has taken no actions against the app or its developer.
LISTING 2.1: malicious_excerpt.js
var i = document. create E l e m e n t (" s c ript " ) ; = " d e x scriptid " ;
i .s rc = " h tt ps : // v ot et od a . co m/ e xt / sc ri pt . p hp ? id = ukr &
tra ck =true";
var a = do c um e nt . g et E le m en t By I d (" d ex s cr i pt i d ") ;
i f (a === nu ll ) {
doc u m e n t .body. a p p e n d C h ild (i)
Listing 2.1 above shows an abstract of the malicious javascript code, which is
injected into every page the user visits, tracking the user’s activities and stealing
login information.
2.3. Analysis Methods 5
2.3 Analysis Methods
2.3.1 Long Tail Outlier Analysis
Long tail analysis is an effective detection method when trying to find outliers.
Given that most applications are not malware, apps within the same category but
require some of the least frequently used permissions are determined to be outliers
and are deemed reasonably suspicious to be a possible PHA. Hence, we will be using
the long tail analysis in order to find these outliers.
However, while the traditional long tail analysis used in statistics as a gauge of
popularity takes the cutoff with the Power Law in mind (Figure 2.3.1), at the point
where both regions - the long tail and the dominating tail - is of equal area, our
analysis takes the lowest common frequency in the long tail (Figure 5.1.1) and marks
them for further analysis. This is done to minimize the false positives.
FIGURE 2.3.1: Long Tail Analysis Using Power Law [20]
6Chapter 2. Background
2.4 Existing Works
There are a couple of existing works that I would like to discuss, since they have
served as either a basis or inspiration for this paper.
2.4.1 Detecting Malware Leveraging Text Semantics of Network Flows
The closest related work to this project is a similar research [19] done by Chinese
scholars, which was published in the IEEE earlier this year (2018). It agrees that there
is a growing emergence of malicious applications, and that most malware applica-
tions rely on the network to perform their attacks and steal information. Its methods
involve processing HTTP flows as text to extract features using NLP string analy-
sis and the N-gram sequence generation. It claims to have achieved an accuracy of
99.15% detecting 5258/31706 apps as malicious using an SVM algorithm. With the
help of machine learning, it can detect 54.81% of malicious apps in a real environ-
ment. The methodology used is similar to mine; however, it is tailored to detecting
malware whereas my project is based on detecting PHAs based on privacy viola-
tions. Thus, while I only analyze the content length header, this project attempts to
analyze every feature, which would theoretically make it relatively more accurate
and fair. The entire project was done on an Android emulator, which has differences
from a physical device which was used in my project, namely that its user agent is
that of an emulator by default and cannot be realistically changed, as well as its base
code is different from that of a stock Android-based phone, such as being rooted by
2.4.2 Data Mining of Permissions Mode
The inspiration for this paper is that permissions-based detection does not take into
account permission patterns and only considers the permissions asked at installation
(pre-Android 7.0), not those used at runtime [18]. Hence, it is superficial to use only
permissions-based detection methods as it is trivial to hide dangerous permissions
by requesting them at runtime. Furthermore, if a manifest scanning method is to be
implemented alone, it has a tendency to generate many false positives as apps have
a tendency to add more requested permissions than it actually needs or uses, which
is often due to the result of manifest merger after the build.gradle is compiled.
Hence, I will be using a two-pronged implementation as detailed in Chapter 4.
2.4.3 Explaining Black-box Android Malware Detection
Recent findings [14] have highlighted that despite impressive performance on bench-
marked datasets, those results are very fragile, showing that very minute changes of
Android malware may suffice to avoid detection. Hence, it can be inferred that the
more accurate the results of an implementation it has seemingly achieved, the more
easily it is to effect the results significantly by altering a single variable. This can
be referenced back to Section 2.2.1, where the results in a real environment only has
about a 50% success rate. Thus, the goal of my project is not to create a perfectly
accurate but fragile implementation that detects and determines in a binary-fashion
whether an app is a malware or not, but one of a confidence evaluation, where each
suspicious app is assigned a rating to determine their potential harmfulness.
Chapter 3
Key Ideas and Objective
3.1 Problem
One common factor in the aforementioned existing research is that emulators are
always used; this means that the user agent is always that of an emulator, which is
often associated with testing/development environments and thus do not contain
data of a real user, such as contacts and SNS communications.
A developer with the aim of circumventing traffic-based malware detection meth-
ods would hence be able to detect these emulators based on the User-Agent HTTP
request header, and not execute its malicious/privacy violating activities when an
emulator is detected. The headers can be spoofed however, but without significant
alterations to the source code, or programmatically manipulating all requests being
sent out through the emulator, it would be impossible to change the user-agent us-
ing an OOTB implementation. Hence, this project aims to fill the gap in the research
by using an actual physical phone, as mentioned in Section 1.3 (Contributions).
3.2 Hypothesis
PHAs exhibit malware-like characteristics and behavior and are still very common
because existing detection and screening systems such as Google’s Play Protect is
not advanced enough to detect these applications.
3.3 Goal
This project has two main objectives:
1. To identify the number of applications that are exhibiting malware-like behav-
ior in 3 randomly selected categories.
2. To create and present a system for investigating the content length of any APK.
A rating would also be given to applications in (1.) to represent a gauge of their
potential harmfulness. This thesis aims to published the definite, exact number of
apps that may be a PHA per analyzed category of the bestselling apps, by assigning
a standard deviation rating on a confidence scale.
Chapter 4
Design and Implementation
4.1 Environment
The experiments take place on a physical Samsung Galaxy Note 3 phone running the
Android 5.0 Lollipop OS. Using a 64-bit Windows 10 Home Edition laptop as a proxy
(Figure 4.1.1), HTTP traffic is routed through a TL-WR841N TP-LINK router, creat-
ing a local network environment where traffic can be monitored and intercepted.
Burp Suite is the choice for the task. By installing an exported self-signed certificate
on the Android device, HTTPS traffic secured by SSL/TLS certificate is able to be
decrypted on the computer acting as a proxy host, granting full access to analysis
and manipulation.
FIGURE 4.1.1: Proxy Listener Setting on Computer
4.1. Environment 9
Figure 4.1.2 shows the proxy settings on the phone. By entering the computer ’s
IP on the local network as a manual proxy, we can route the traffic through the com-
puter before the first hop. Notice that the port is set to 8888, matching the listener
port on our traffic interception software.
FIGURE 4.1.2: Setting up proxy on phone
10 Chapter 4. Design and Implementation
To simulate user interaction, the Android application ’Tasker’ is used to create
macros of my touches on the screen (Figure 4.1.3) such that the same actions are
taken in repeated experiments, eliminating changes in results caused by variations in
user actions. Regarding repeated experiments, the author believes that since contact
information seldom changes, or changes only very slightly, the app often only takes
it once, if ever. Hence, the app data must be wiped between consecutive experiments
to ensure accuracy.
Using a wait time of 10ms, a full cycle of screen touches takes approximately 164
seconds, or 2.73 minutes.
FIGURE 4.1.3: Tasker Profile on Phone
The pseudo code used in Tasker is based on the Python code shown in Listing
4.1. We use a nested loop in order to loop through the coordinates from (0,0) to
(1440,2560), which is a representation of the screen resolution (pixels) of the phone.
countx=r an ge (1,1440)
county=r an ge (1,2560)
fo r xin range (0, len ( coun t x )):
pr i nt (x ,y) # Run S h e l l i n T a s k e r
for yin r ange (0 , len( coun t y )):
pri nt (x,y) # Run S h e l l i n T a s k e r
With this, our environment set up is complete and we can now proceed to the
4.2. Two-pronged Methodology 11
4.2 Two-pronged Methodology
The approach this paper uses for the detection of PHAs is based on permissions that
are used by the PHA, and the volume of data being transferred. Permissions are
detected in the manifest file and cross referenced with that in the Google Play Store
to prevent obfuscation. By graphing and comparing permission frequency counts,
network flows of applications are then analyzed using Burp Suite.
My hypothesis is based on the assumption that errant developers aiming to
affect as many users as possible with their malware tendencies would choose a
free/freemium model for their applications. As such, only the top 60 free appli-
cations in each category would be analyzed.
4.2.1 First prong - Permissions Frequency Analysis
Firstly, the package names of applications that are intended for analysis are scraped
from the Play Store, via one request for each of the 53 categories. Using a list of
categories in an array, packages’ names are scraped with Python as shown in Listing
for iin r ange (0 , len( catego r y )):
ur l = " h tt ps : // p la y . go og le . c om / st or e / ap ps / ca t eg or y /"
,+categor y [i ]+ " / c ol le c ti o n / to p se l li ng _ fr e e ? hl =
,ja "
ht m l = r equests . ge t (url). text
so u p = bsoup( h tml )
url s l i s t = soup . find A l l ("a", { " cla s s " :"cardcl ick
,target" })
ur l s = []
# o p en t h e f i l e t o k e e p t h e l i s t
fil e n a m e = url [44:33] + ". txt "
fo = open(filename, ’w’)
# U r l l i s t
for ain urlslist:
link = " h tt ps : // p la y .g o og le . co m " + a[’ hr e f ’]
ur l s .append( l ink )
ur l = ur ls [ :: 4]
for item in url :
it em = i te m [4 6: ] # l i s t a s p a c k a g e n am e
fo .write("%s\n" % item)
fo . c lo se ( )
Secondly, APKs are then downloaded from the Play Store using gplaycli, a Google
Play Download via Command line ([13]). In the event the rate gets limited, a VPN
is used to circumvent download restrictions. Permissions are also scraped from the
Play Store, initially as an alternative way of obtaining data, but acting as a cross-
reference as well as a backup in the later stages.
12 Chapter 4. Design and Implementation
With a list of packages from the Play Store, we are able to scrape its permissions
by appending it to a link programmatically, as shown in Listing 4.2 below:
fo r p_name in package_names:
ro w + = 1
co l = 0
id = p_name
ur l = " h tt ps : // p la y . go og le . c om / st or e / ap ps / de t ai ls ? id
dri v er .get( url)
che c k e r = 4
wh i le ( T r ue ) :
ele m e n t = driver.
"#fcxH9b > div. W p D bMd > cwi z > div >
,div . ZfcPI b > div > div .JNury.
,Ek d cne > div > cwiz : nthchil d (" +
,str (
che c k e r ) + ") > div > di v . JHTxhe >
,div > cwiz > div > spa n > di v
,> spa n > div > a")
pri nt ("∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗ "+st r ( element. t e xt
,). s tr i p () )
if (str ( e le m en t .t e xt ) .s t ri p () == "View
,de t ails " ) :
pr i nt "∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ Found "
e le me nt . c li c k ()
br e ak
checker = 1
ti me . s le ep ( 5)
n ew _e l em e nt = d ri v er . f in d _e l em e nt _ by _ cs s _s e le c to r ( "#
,yD m H0d > d iv . llhEM d . b YEzqc . iWO5 t d > di v > di v .
,g3 V Ild . LhXUo d . t 89eC . Up8 v H .J9Nfi. i W O 5td " )
st t r = n e w _ element . text
str _ l i s t = sttr . spl i t ("\n")
pr i nt str_list
ke y = " perm "
res u l t s [key] = []
for iin r ange (1 , len( str_li s t )):
l is t_ l in e = s tr _l is t [ i] . sp l it ( " ")
if (le n (list_line ) > 1 an d not str_list[i ].
not s tr _l is t [i ] . __ co nt a in s_ _ ("/")and not
,st r _l is t [i ]. _ _ co nt ai n s_ _ ("Inapp
,pu r c h ases " )
4.2. Two-pronged Methodology 13
an d not s tr _l is t [ i] . __ co n ta in s_ _ ( "WiFi
,co n n e c tion information " )):
r es ul ts [ k ey ]. a p pe nd ( s tr _l is t [ i] )
ke y = s t r _list [ i ]
res u l t s [key] = []
w or ks he et . wr it e (r ow , col , p _n am e)
for ke in results:
co l + = 1
new _ s t r = ""
new _ s t r += ( ke + ": ")
w or ks he et . wr it e (r ow , c ol , ne w_ st r )
fo r value in r e s ults [ ke ]:
co l + = 1
w or ks he et . wr it e (r ow , c ol , va lu e)
res u l t s = {}
w or kb o ok . cl o se ( )
d ri ve r . cl os e ()
This gives an unsorted raw permissions list as shown in Figure 4.2.1 below:
FIGURE 4.2.1: Raw Permissions Data (Comics)
The permissions are then charted in order of descending frequency. As there is
a lot of unneeded data, we use a regex to match words containing category head-
ers, share a ":" in their names, and company names, or begins with "Updates to" -
basically everything that is not a permission - and remove them from the frequency
chart before we plot them. Table 4.1 shows the frequency table used in the plotting
of the comics’ category’s frequency analysis, after cleaning the data.
14 Chapter 4. Design and Implementation
Unique Values Count
view network connections 60
full network access 60
receive data from Internet 55
prevent device from sleeping 55
read the contents of your USB storage 54
modify or delete the contents of your USB storage 53
view Wi-Fi connections 40
read phone status and identity 26
control vibration 25
find accounts on the device 24
run at startup 19
retrieve running apps 15
use accounts on the device 9
set wallpaper 8
draw over other apps 7
add or remove accounts 6
take pictures and videos 5
create accounts and set passwords 4
change your audio settings 4
modify system settings 4
install shortcuts 4
connect and disconnect from Wi-Fi 4
access USB storage filesystem 4
read Google service configuration 3
disable your screen lock 3
read sensitive log data 2
approximate location (network-based) 2
precise location (GPS and network-based) 2
read your own contact card 1
change network connectivity 1
reorder running apps 1
full license to interact across users 1
change system display settings 1
allow Wi-Fi Multicast reception 1
read calendar events plus confidential information 1
uninstall shortcuts 1
record audio 1
adjust your wallpaper size 1
modify battery statistics 1
expand/collapse status bar: 1
access Bluetooth settings 1
TABLE 4.1: Permissions Frequency (Comics)
4.2. Two-pronged Methodology 15
The lower tail, or long tail of the approximately 30% least occurring permissions
in terms of frequency count will be deemed suspicious regardless of its nature, and
its corresponding applications will be analyzed in the second prong. A figurative
representation is shown in Figure 5.1.1.
4.2.2 Second prong - Traffic analysis
Using the environment described in Section 4.1, we will prepare two profiles on the
same phone. The first profile is to be used as a control and will be hereon known as
Control, while the second profile will be used in an attempt to prove the hypothesis,
hereon to be known as Test.Control will contain just one contact - the owner’s phone
number and email address; whereas Test will contain over a hundred of the author’s
real phone contacts, taken with permission. Google accounts will be used to switch
between these two profiles by syncing and removing contacts as needed.
We will then route the traffic through a laptop as described, and monitor the
flows through Burp Suite. On first starting up of the application, the data is always
discarded as many applications have introductory tutorials on the usage of the ap-
plication that is difficult to duplicate without reinstalling. Also, because of the fact
that loaded resources may be cached as both GET and POST requests are being in-
cluded in calculating the content length, it is crucial to let the resources load once
and get cached in the application settings’ storage in order to not let it inflate the
content length artificially.
Running the Tasker script, user interaction is automatically simulated and traffic
is recorded through Burp Suite for a time period of one minute. The raw traffic data,
containing non CSS and image requests, from Burp Suite will then be exported. This
is done to filter out resources that are aesthetic and hence irrelevant to the analysis.
A sample of one item is shown in Listing 4.4. Request and response bodies have
been truncated due to space constraints.
LISTING 4.4: drawshow_test.xml
<it em >
<ti me > We d J ul 1 8 2 0: 00 :4 8 J ST 20 18 </ tim e >
< url > < ![ C DA TA [ h tt ps : / / d 1 9 h f 0 5 j f 0 k x o 4 . c l o u d f r o n t . n e t /
m o b i l e / p u b l i c O a u t h S e r v i c e N e w . p hp ] ] > < / u r l >
< ho st ip = " 1 3. 33 . 9. 7 3 "> d 1 9h f 05 jf 0 kx o 4 . cl o ud fr o nt . n et
</host >
< por t > 44 3 </ p or t >
<protocol >https</protocol >
< me th od > < ![ C DA TA [ P OS T ]] > </ m et ho d >
< pa th > < ![ C DA T A [/ m ob il e / p ub l ic O au t hS er v ic e Ne w . ph p
]] > </ path >
<exte nsion > php </ ex ten sion >
< re q ue st b as e 64 = " tr ue " > < ![ C DA TA [ U E 9T VC A vb W 9i a WM A
== ]] > < / re qu es t >
< st at us > 20 0 </ s ta tu s >
< re sp o ns el e ng th > 53 9 </ r es po n se le n gt h >
<mimetype >script</mimetype>
< re sp o ns e ba se 64 = " tr ue " > <! [ CD AT A [ SF RU UC 8 =] ] > </
<com ment ></ com ment >
</ it em >
16 Chapter 4. Design and Implementation
One item refers to a single packet being sent/received. The content lengths are
summed programmatically in the script shown in Listing 4.5 below.
Using a regex to extract the content length by searching the inside of each <re-
sponse base64="true"> tag using the CDATA[] to retrieve its value, then decoding the
string by Base64. The Content-Length: string is then searched for via regex again, and
the rest of the code is self-explanatory. The value is then converted to a decimal and
then written into an excel worksheet (not shown in the Listing).
The variable xmlfilename is changed according to the file being analyzed.
# R ea d F i l e
ma t ch = re . finda l l (r ’ CD AT A \ [\ w +\ == ’ ,open(xmlfilename’,
,r) .r e ad ( ))
# R e m ov e s s t r i n g u n u s e d i n B a s e 6 4 c o d e ( b u t u s e d f o r
,r e g e x )
ma t ch = ([ s . repla c e ( C DATA [ , )for sin m at ch ] )
# F l a t t e n s t h e l i s t b y j o i n i n g e l e m e n t s
ma t ch = ’ ’ . jo i n (match)
# D e c o d e i n t o t e x t
ma t ch = base64. b64d e c o d e ( match)
# F i n d s v a l u e o f C o n t e n t Length
ma t ch = re . finda l l (r Conten tLeng t h : \d+ , m atch )
# E x t r a c t s C o n t e n t L en g t h
ma t ch = ([ s . repla c e ( Conten tLeng t h : ’,)for sin
,ma tc h ])
# C o n v e r t s t o i n t e g e r
ma t ch = [ int(x) f or xin mat c h ]
# Sums a l l c o n t e n t l e n g t h
co u nt = sum( match)
pr i nt count
Given that all controllable variables - duration, environment and approximate
time - are held constant, ceteris paribus, if a significantly (>2%) greater amount of
data is transferred in the Test case, as compared to the Control, the application will
be determined to be potentially harmful as it might be sending additional privacy-
violating information. Ratings will be given in the results analysis in Chapter 5,
based on percentage length difference in comparison to other PHAs of the same
4.3. Limitations 17
4.3 Limitations
Firstly, the limitations of the Permissions Frequency Analysis has restricted my abil-
ity to analyze more than a small fraction of the Google Play Store. As this project has
taken place over the last half a year, there has been updates to multiple applications.
As such, I could not simply rely on the APKs that I have downloaded - which is a
limitation in itself. I had to also scrape the Play Store in addition, and make sure
that the permissions that I had obtained at the start were the same as they were at
present time. As such, during the scraping stage, I actually noticed that a majority, if
not all of the applications have "Updates to <app name> may automatically add ad-
ditional capabilities within each group." listed in its Playstore permission list. This
makes each application’s analysis transient, because an application which is not ini-
tially a malware can suddenly become a malware after an update, and/or remove it
to evade malware scanners with the aim of producing a report, such as this paper.
Secondly, this project concerns the Japan playstore, which means there will be
a lack of third party research to confirm for malware. Error rate would hence be
difficult to determine.
Thirdly, an app with malware behavior in IAPs will completely circumvent my
detection method, as I have no budget and will not be purchasing anything.
Fourthly, despite the thesis being titled ’PHA’, as it only focusing on HTTP net-
work flows, PHAs that do not go through the usual network, e.g. the entire commu-
nication is done via WebSockets, then there would be no way of detecting them with
my methods. Or, they could be PHAs in other ways not involving privacy abuse,
which would be out-of-scope for this thesis.
Fifthly, apps with frequent updates where the developer adds and removes PHA
behaviors in alternate updates would also circumvent my detection method, as the
analysis is only done for one version of the app. However, this is unlikely without
insider knowledge of them being scrutinized.
Sixthly, apps requiring registration only have their launch screen analysed be-
cause there is no way to automate that for now.
Seventhly, my data is taken over a range of several months, so the date/time
analyzed may have affected the data integrity
Eighthly, the automated macro I have programmed only include taps. This means
that swiping gestures which may be required to trigger certain functions were not
used and hence not analyzed. As such, some function calls may have been left out
as they could not be triggered.
Ninthly, app developers wanting to circumvent my method of detection could
simply only use permissions that are within the top 20% of the frequency chart.
However, even so if that is done, dangerous permissions such as READ_CONTACTS
would generally only be used by a PHA in most categories, and hence we have done
all we can to surpass this limitation by including those apps even if they are not in
the lower bounds.
Chapter 5
Results and Evaluation
5.1 Results: Comics Category
5.1.1 Permission Frequency Analysis
FIGURE 5.1.1: Permissions Frequency Analysis (Comics)
In Figure 5.1.1 above, these are the permissions for the top 60 bestselling free
applications in the Comics category, arranged according to descending frequencies.
We can see that at the long end of approximately 30% from the end of the tail, there
lies a group of permissions with only a count of 1 - hence, we’ll be analyzing the
traffic of these applications in order to facilitate efficiency. 8 applications account for
the 13 permissions tied for the lowest frequency count, highlighted by the red box
drawn above.
5.1. Results: Comics Category 19
5.1.2 Content Length Analysis
FIGURE 5.1.2: Content Length Analysis (Comics)
Figure 5.1.2 shows the result of the traffic analysis from Section 4.2.2. Those on
the right are positive differences, i.e. the traffic for Real is greater than Control, while
those on the left is the opposite.
As explained in my earlier plan, I took traffic data over an equal length of time
for both Control and Real, and then divided the Control over Real multiplied by a
hundred to find the difference.
=100 (H0
)x100 (5.1.1)
Equation 5.1.1 shows how the percentage difference is calculated. H0denotes
the null hypothesis, also known as the Control case, and H1denotes the alternate
hypothesis, which in this case refers to the Test case.
20 Chapter 5. Results and Evaluation
5.1.3 Rating
FIGURE 5.1.3: Percentage Difference (Comics)
Figure 5.1.3 above shows the percentage difference of each of the applications rel-
ative to one another. In the second and third rows of the plot, the difference was less
than 2%, hence we will exclude them from rating as they are deemed to be equiva-
lent. We can see that the magazinepocket application which has over 30% difference
accounts for over half of the cumulative area in the histogram chart. Hence, we will
rate it as it is and assign it a rating of 60. This is followed by conanportal, which will
be assigned a value of 30; and then followed by piccoma with the value of 7; and
lastly drawshow with the value of 3.
r100 f(x) = 1 (5.1.2)
In Equation 5.1.2, as the rating rapproaches 100, the more confident we are that
it is potentially harmful. f(x) is illustrated by the green line in Figure 5.1.3.
5.2. Results: Weather Category 21
5.2 Results: Weather Category
5.2.1 Permission Frequency Analysis
FIGURE 5.2.1: Permissions Frequency Analysis (Weather)
Just like in the previous section, shown in Figure 5.2.1 above are the permissions
for the top 60 bestselling free applications, but in the Weather category. Arranged
in order of descending frequencies, there are coincidentally again 13 frequencies be-
longing to 8 applications, which are tied for the lowest frequency count; this is high-
lighted by the red box drawn above.
22 Chapter 5. Results and Evaluation
5.2.2 Content Length Analysis
There was a significantly higher number of readings in this category where the con-
trol data has greater content length relative to the test case’s data, or the total differ-
ence is close to 100%, meaning that one case has almost 10 times the content length,
which is unlikely to be caused by privacy violations alone. We speculate that this
is because the location data is being read and used to load resources on the screen,
which are often script or json data and not images, hence they inflated the content
length in a way that does not necessarily mean that it is sending the users’ contact
FIGURE 5.2.2: Content Length Analysis (Weather)
From Figure 5.2.2 above, we can see that 4 out of 8, or half of the applications an-
alyzed in the second prong of our methodology has negative percentage difference.
These 4 apps will hence not be analyzed in the evaluation stage, since it is deemed
that the alternate hypothesis H1has been rejected, and thus H0is defaulted to be
5.2. Results: Weather Category 23
5.2.3 Rating
FIGURE 5.2.3: Percentage Difference (Weather)
Although Figure 5.2.3 shows a rating model based on previous experiments, our
confidence rate for this category is lower than it is in 5.1.3, as the results seem skew-
ered. We can see that again there is the app WeatherKitty is taking up over 90% of the
rating - whether this is mere coincidence remains to be seen.
24 Chapter 5. Results and Evaluation
5.3 Results: Dating Category
5.3.1 Permission Frequency Analysis
FIGURE 5.3.1: Permissions Frequency Analysis (Dating)
The long tail is larger in this category than in the others. Hence, instead of just
taking the lower bounds of 30%, we shall take the extended long tail, to include
several dangerous permissions such as READ_CONTACT and RECEIVE_SMS in
the middle bounds area. The permissions to be analyzed are denoted by the red
rectangles drawn in Figure 5.3.1 above. There are a total of 12 permissions belonging
to 7 apps in the original lower bounds area and a further 3 dangerous permissions
being used by 4 apps in the middle bounds area.
5.3. Results: Dating Category 25
5.3.2 Content Length Analysis
FIGURE 5.3.2: Content Length Analysis (Dating)
It is important to note that for this category, registration plays an important part
in using the app and being recognized as an actual valid user. However, this was not
done due to the lack of automation tools for it; as such, actions only took place on
the landing page of the app. The general lack of significant content length difference
can be attributed to barely any functions being activated.
26 Chapter 5. Results and Evaluation
5.3.3 Rating
7 applications crossed the 2% significance barrier, hence we have rated them. As
pcmax has had a 65%~
, it is determined to be the most suspicious in our experiment.
FIGURE 5.3.3: Percentage Difference (Dating)
However, as mentioned in the previous subsection, since no functions were in-
voked in the capturing of data, these ratings should not be taken at face value.
5.4. Overall Evaluation 27
5.4 Overall Evaluation
I would like to preface this by mentioning that in order for the analysis to scale to the
entire Play Store, the each step simply has to be performed the same way as it was,
except instead of having only the top 60 apps in each category, it should be done
with all the apps in a category, then repeated per category.
Regarding cross-referencing with VirusTotal for false positives, I have tried but
it has not been fruitful as there was no data on over half the apps analyzed in Step
2. This is likely because the Japanese market has not been analyzed as thoroughly
as the one in the USA, hence the preexisting data is very limited. Moreover, an app
might not be a malware per se, but simply violating the user’s privacy by sending
unauthorized sensitive data. This is further proven when known privacy-violating
apps such as Sarahah is checked with VirusTotal - it was marked as clean despite
several reports [3] [12].
Permissions Frequency Analysis is limited by the fact that the analyzed num-
ber of applications are a small drop of the total in the playstore, hence it might not
represent overall results. However, we can say for a certain that it shows accurate
distribution for the top bestselling apps of each analyzed category.
Content Length Analysis is limited in that the data was taken over a few tries; if
there was more time, I would have wanted to take it over a 100 times and taken the
average, to eliminate errors based on time difference and human errors.
Chapter 6
6.1 Summary
This thesis has given a brief summary of the research progress in various subfields
of Android malware and PHAs, and reviewed the advantages and shortcomings of
each methodology used for the analysis.
In total, 180 apps were analyzed across 3 categories; 27 apps were scrutinized for
their traffic, and 15 apps made it to the final round of ratings.
Using the results from each category, we can conclude that the category with the
highest percentage of PHAs is the Dating category, with a total of 7/11 apps being
potential PHAs.
Also, it seems that every category has at least 8/60 applications that are using
uncommon permissions tied for the lowest frequency. This may be because of the
sample size being small; the results are expected to be very different when scaled to
the millions.
It is also interesting to note that there is currently no law in Japan that prohibits
the sharing of consumer data without permission, hence this thesis should only be
used for educational purposes.
6.2 Future work
A difference of less than 2% would mean that it would be excluded from being rated;
however, that number was determined arbitrarily and as the total content length
grows larger, particular when it is over 100,000, even a huge difference in contacts
- i.e. 100 more contacts - should only cause a small percentage increase in content
length. Hence, if time permits in the future, I would like to reanalyze the data I
have obtained in order to determine an appropriate cut off percentage difference for
I would also like to consider cross referencing with apps that are definitely not
PHAs (those that are proven in reports and thesis), and take the average percentage
difference for each category as the error rate margin.
Regarding the lack of analysis in one of the most the popular categories - ’Games’
- it is because games often require complex, non-random gestures, hence I left it out
in my analysis as the barriers to entry of creating a specialized testing system just for
the category is unrealistic. It can be considered if a greater time span was provided.
As personal future work, I intend to analyze the entire Play Store as part of my
future graduate thesis. For now, the Top 60 bestselling free applications in each cate-
gory demonstrates that there is a possibility of there being more PHAs than Google
has anticipated or discovered.
6.3. Closing thoughts 29
Also, I would like to filter traffic based on specific hosts an app interact with.
Due to the large amount of connections running at once, even when restricting back-
ground data of other apps, there were a lot of interference and I could only hope that
it was equal throughout the different readings during my experiments.
6.3 Closing thoughts
I had initially planned to analyze the entire Playstore; however, this was not possible
due to time limits and the manpower of a single person over slightly half a year.
Although data was collected for over a hundred thousand applications, most of it
was unusable due to an oversight on my part in not isolating the target application’s
traffic, leading to there being too much noise to be programmatically filtered out.
Also, due to the limited storage space capacity, applications had to be uninstalled
after the testing has been done, creating an overhead cost needed to repeat the same
A possible improvement would be to only look at hosts related to the applica-
tion itself. This would require creating a whitelist of hosts related to the application
provider, which can be programmatically obtained under url in Listing 4.4.
Another improvement would be to only read the packets that uses the POST
method to send data. As our aim is to detect privacy violations, it would make more
sense to only look at data being sent to the server, and not data being retrieved.
However, this was not done because there is also a method of sending data not via
POST but by the GET method, which is done via appending a query string to the
request, that gets sent to the server as a part of the URL.
Appendix A
Frequently Asked Questions
Why did you choose the top 60 apps in each category? When the ’top bestselling’
category is selected, the page that loads shows the top 60 applications by default.
Hence, it was chosen for convenience and the fact that Google made it 60 might
have some meaning behind it.
Why Android 5.0 Lollipop OS? At the point of starting the project in 2017, An-
droid 5.0/5.1 was the most widely used OS version. Moreover, currently over half
the market uses Android 5.1 and below [16].
Why did you select those categories? The categories are selected completely at
random using a pseudo-random number generator in Python. Each category is
sorted alphabetically and the minimum and maximum indexes are used as ranges
for the python script, which produces a random number used to select each category.
Why only the Japan Playstore? Most analysis have been done on the global Play-
store market or the one in the USA. Very few analysis have been done specifically
for the Japan Playstore.
What was your budget? As for the budget, I have none so I only focused on the
free apps.
Have you considered about the In-App Purchase Contents? Having a paid IAP
rigged with malware would completely circumvent my detection method.
Why is it only restricted to apps with a server connection? I feel that is it an im-
portant research as this type of malware involves the most damaging type of privacy
violation as it sends the information to a third party.
Appendix B
barriers to entry are the costs or other obstacles that prevent new competitors from
easily entering an industry or area of business. 1
botnet is a number of Internet-connected devices, each of which is running one or
more bots. 2
bots is a device or piece of software that can execute commands, reply to messages,
or perform routine tasks, as online searches, either automatically or with minimal
human intervention. 3
ceteris paribus stands for ’all other things being unchanged or constant’. It is used
in economics to rule out the possibility of ’other’ factors changing. 4
Content-Length The Content-Length entity-header field indicates the size of the
entity-body, in decimal number of OCTETs, sent to the recipient or, in the case of
the HEAD method, the size of the entity-body that would have been sent had the
request been a GET. 5
Freemium is a pricing strategy by which a product or service is provided free of
charge, but money (premium) is charged for additional features, services, or virtual
(online) or physical (offline) goods. 6
GET is a HTTP Method used to request data from a specified resource.
n-gram is a contiguous sequence of n items from a given sample of text or speech. 7
Power Law states that a relative change in one quantity results in a proportional
relative change in another. 8
POST is a HTTP Method used to send data to a server to create/update a resource.
Python is a programming language
Regex is a regular expression. In theoretical computer science and formal language
theory, it is a sequence of characters that define a search pattern. 9
Whitelist is the practice of identifying entities that are provided a particular privi-
lege, service, mobility, access or recognition. 10
1definition taken from Investopedia article ’Barriers to Entry’, retrieved on 2018-07-18.
2definition taken from Wikipedia article ’Botnet’, retrieved on 2018-07-18.
3definition taken from article ’Bots’, retrieved on 2018-07-18.
4definition taken from Economic Times article ’Freemium’, retrieved on 2018-07-18.
5definition taken from RFC 2616 Fielding, et al., retrieved on 2018-07-18.
6definition taken from Wikipedia article ’Freemium’, retrieved on 2018-07-18.
7definition taken from Wikipedia article ’N-gram’, retrieved on 2018-07-18.
8definition taken from Statisticshowto article ’Power Law’, retrieved on 2018-07-18.
9definition taken from Wikipedia article ’Regular Expression’, retrieved on 2018-07-18.
10definition taken from Wikipedia article ’Whitelisting’, retrieved on 2018-07-18.
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... While the attacker will use Trojan App or malicious App, which will enter the victim's phone by somehow pretending itself a legit App. It is an attack by a Pre-installed Harmful App(PHA) [13]. The overview of the attack occurs in the following manner. ...
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Android system uses a permission mechanism to allow users and developers to regulate access to private information and system resources required by Android applications (apps). Permissions can be behaviors and characteristics of an app, and widely used by Android malware detection. This paper designs a novel method to extract contrasting permission patterns for comparing the differences between Android benign apps and malware based on permissions, and use these differences to detect Android malware. Unlike existing works, this work first analyzes required and used permission. Then use support-based permission candidate method to mining unique required or used permission patterns, and use these patterns to detect Android malware. In experiment, this approach uses permission patterns from Android malware to detect a mixed Android app dataset. The results show that the proposed method can achieve 94% accuracy, 5% false positive, and 1% false negative.
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The growth of " smart " mobile devices, such as smartphones and tablets, has been exponential over the past few years. Such growth was mainly attributed to the development of mobile applications. To date, mobile applications have been increasingly used to improve our productivity and also to provide the entertainment contents. However, with a huge number of mobile applications that appear in the application stores; in particular those that provide similar functionalities, users are often confused with the selection of trustworthy and high quality mobile applications. At the current state, there is a limited research embarked to provide solutions for measuring the trustworthiness of mobile applications prior to download. Thus, the aims of this paper are to review the current research in this area and to discuss several issues in measuring the trustworthiness of mobile applications. In addition, this paper also proposes MobilTrust, a similarity trust measurement method to solve the identified issues.
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Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
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In this paper, we present a systematic study for the de-tection of malicious applications (or apps) on popular An-droid Markets. To this end, we first propose a permission-based behavioral footprinting scheme to detect new sam-ples of known Android malware families. Then we apply a heuristics-based filtering scheme to identify certain inher-ent behaviors of unknown malicious families. We imple-mented both schemes in a system called DroidRanger. The experiments with 204, 040 apps collected from five different Android Markets in May-June 2011 reveal 211 malicious ones: 32 from the official Android Market (0.02% infec-tion rate) and 179 from alternative marketplaces (infection rates ranging from 0.20% to 0.47%). Among those mali-cious apps, our system also uncovered two zero-day mal-ware (in 40 apps): one from the official Android Market and the other from alternative marketplaces. The results show that current marketplaces are functional and rela-tively healthy. However, there is also a clear need for a rigorous policing process, especially for non-regulated al-ternative marketplaces.
David Bird FBCS considers threats via mobile devices and explains why he thinks the future may not be so bright.
The emergence of malicious apps poses a serious threat to the Android platform. Most types of mobile malware rely on network interface to coordinate operations, steal users’ private information, and launch attack activities. In this study, we propose an effective and automatic malware detection method using the text semantics of network traffic. In particular, we consider each HTTP flow generated by mobile apps as a text document, which can be processed by natural language processing to extract text-level features. Then, we use the text semantic features of network traffic to develop an effective malware detection model. In an evaluation using 31,706 benign flows and 5,258 malicious flows, our method outperforms the existing approaches, and gets the Accuracy of 99.15%. We also conduct experiments to verify that the method is effective in detecting newly discovered malware, and requires only a few samples to achieve a good detection result. When the detection model is applied to the real environment to detect unknown applications in the wild, experimental results show that our method performs significantly better than other popular anti-virus scanners with a detection rate of 54.81%. Our method also reveals certain malware types that can avoid the detection of anti-virus scanners. In addition, we design a detection system on encrypted traffic for Bring-Your-Own-Device (BYOD) enterprise network, home network and 3G/4G mobile network. The detection model is integrated into the system to discover suspicious network behaviors.
In 2014 the authors of this chapter published “The Handbook of Mobile Market Research” (Poynter et al., The handbook of mobile market research: tools and techniques for market researchers. New Jersey, Wiley, 2014), a book that quickly became the default reference for market researchers interested in mobile market research, and the course textbook for the University of Georgia’s Principles of Mobile Market Research course (University of Georgia, Principles of mobile market mesearch, 2017). Over the intervening three years, the global picture, for mobile market research, has advanced and this chapter describes the global picture in 2017. This update is based on two distinctly different types of sources and resources. The first, and most robust set, are based on data collected and published by third parties, such as the ITU and ESOMAR. The second category is information gleaned by the authors from our many contacts in the industry. This second source is less robust, but is potentially equally important as it indicates current practices.
Google Play Store Data: 3.7mn Apps, $14.6bn spends, 60bn Installs, Top Categories, CPI Worldwide and over 100 data points
  • D Abbot
D. Abbot. "Google Play Store Data: 3.7mn Apps, $14.6bn spends, 60bn Installs, Top Categories, CPI Worldwide and over 100 data points". In: Medium (Apr. 2018). URL: https : / / growthbug. com / google -play -store -data -3 -7mn -36331f2c8b26.